IDEAS home Printed from https://ideas.repec.org/a/wly/agribz/v40y2024i4p885-907.html
   My bibliography  Save this article

Analysis of pet‐food customer postpurchase experience using online customer reviews: Implications for product and marketing strategies

Author

Listed:
  • Lonnie Hobbs
  • Aleksan Shanoyan
  • Zelia Z. Wiley
  • Greg Aldrich

Abstract

Pet‐food industry growth and demand for specialized product offerings have generated new opportunities for companies to enhance their competitiveness and profitability through effective product differentiation. Recent rise in e‐commerce and technological advancements for capturing and analyzing online customer review data provide new opportunities for large‐scale assessment of customer perceptions of product attributes. This paper presents an integrated qualitative and quantitative approach for utilizing online customer review data to generate insights for informing pet‐food industry product and marketing decisions. The results from the analysis of 5632 online reviews of two pet‐food products highlight (i) product attributes that effectively differentiate (or put on par) two competing pet‐food products, (ii) identify product attributes that are most important for customer postpurchase experience, and (iii) estimate the effect of specific attributes on customers' positive (or negative) postpurchase experience. Specific pet‐food attributes examined include packaging, health/benefit, ingredient, taste, smell, appearance/form, natural/organic, processing, sourcing, small/large breed, price, and service. The advantages of this approach include its ability to leverage large amounts of easily accessible data and its generalizability to the analysis of other consumer goods that have significant online sales and customer review data [EconLit Citations: Q13, M31].

Suggested Citation

  • Lonnie Hobbs & Aleksan Shanoyan & Zelia Z. Wiley & Greg Aldrich, 2024. "Analysis of pet‐food customer postpurchase experience using online customer reviews: Implications for product and marketing strategies," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 885-907, October.
  • Handle: RePEc:wly:agribz:v:40:y:2024:i:4:p:885-907
    DOI: 10.1002/agr.21866
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/agr.21866
    Download Restriction: no

    File URL: https://libkey.io/10.1002/agr.21866?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    2. Tianjun Feng & L. Robin Keller & Liangyan Wang & Yitong Wang, 2010. "Product Quality Risk Perceptions and Decisions: Contaminated Pet Food and Lead‐Painted Toys," Risk Analysis, John Wiley & Sons, vol. 30(10), pages 1572-1589, October.
    3. Matthew A. Barlow & J. Cameron Verhaal & Ryan W. Angus, 2019. "Optimal distinctiveness, strategic categorization, and product market entry on the Google Play app platform," Strategic Management Journal, Wiley Blackwell, vol. 40(8), pages 1219-1242, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Camerani, Roberto & Corrocher, Nicoletta & Fontana, Roberto, 2020. "It's never too late (to enter)… till it is! Firms’ entry and exit in the digital audio player industry," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    2. Liuan Wang & Lu (Lucy) Yan & Tongxin Zhou & Xitong Guo & Gregory R. Heim, 2020. "Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study," Information Systems Research, INFORMS, vol. 31(2), pages 537-555, June.
    3. M. Narciso, 2022. "The Unreliability of Online Review Mechanisms," Journal of Consumer Policy, Springer, vol. 45(3), pages 349-368, September.
    4. Joshua Chang, 2025. "The mediating role of brand image in the relationship between storytelling marketing and purchase intention: case study of PX mart," Future Business Journal, Springer, vol. 11(1), pages 1-14, December.
    5. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    6. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
    7. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    8. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    9. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    10. Caldieraro, Fabio & Cunha, Marcus, 2022. "Consumers’ response to weak unique selling propositions: Implications for optimal product recommendation strategy," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 724-744.
    11. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    12. Tolga Akcura & Kemal Altinkemer & Hailiang Chen, 0. "Noninfluentials and information dissemination in the microblogging community," Information Technology and Management, Springer, vol. 0, pages 1-18.
    13. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    14. Ruixin Ding & Bowei Chen & James M. Wilson & Zhi Yan & Yufei Huang, 2023. "SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market," Papers 2401.05395, arXiv.org.
    15. Xiaowei Mei & Hsing Kenneth Cheng & Subhajyoti Bandyopadhyay & Liangfei Qiu & Lai Wei, 2022. "Sponsored Data: Smarter Data Pricing with Incomplete Information," Information Systems Research, INFORMS, vol. 33(1), pages 362-382, March.
    16. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    17. Xiaole Dou & Zenglu Li & Chun Liu, 2022. "Secondhand product quality disclosure strategy of the retailer under different supply chain structures," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2982-2999, October.
    18. Yuangao Chen & Shuiqing Yang & Zhoujing Wang, 2016. "Service cooperation and marketing strategies of infomediary and online retailer with eWOM effect," Information Technology and Management, Springer, vol. 17(2), pages 109-118, June.
    19. He, Qiao-Chu & Chen, Ying-Ju, 2018. "Dynamic pricing of electronic products with consumer reviews," Omega, Elsevier, vol. 80(C), pages 123-134.
    20. Makoto Nakayama & Yun Wan, 2019. "Same sushi, different impressions: a cross-cultural analysis of Yelp reviews," Information Technology & Tourism, Springer, vol. 21(2), pages 181-207, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:agribz:v:40:y:2024:i:4:p:885-907. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1520-6297 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.